27,073 research outputs found
How did the discussion go: Discourse act classification in social media conversations
We propose a novel attention based hierarchical LSTM model to classify
discourse act sequences in social media conversations, aimed at mining data
from online discussion using textual meanings beyond sentence level. The very
uniqueness of the task is the complete categorization of possible pragmatic
roles in informal textual discussions, contrary to extraction of
question-answers, stance detection or sarcasm identification which are very
much role specific tasks. Early attempt was made on a Reddit discussion
dataset. We train our model on the same data, and present test results on two
different datasets, one from Reddit and one from Facebook. Our proposed model
outperformed the previous one in terms of domain independence; without using
platform-dependent structural features, our hierarchical LSTM with word
relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively
to predict discourse roles of comments in Reddit and Facebook discussions.
Efficiency of recurrent and convolutional architectures in order to learn
discursive representation on the same task has been presented and analyzed,
with different word and comment embedding schemes. Our attention mechanism
enables us to inquire into relevance ordering of text segments according to
their roles in discourse. We present a human annotator experiment to unveil
important observations about modeling and data annotation. Equipped with our
text-based discourse identification model, we inquire into how heterogeneous
non-textual features like location, time, leaning of information etc. play
their roles in charaterizing online discussions on Facebook
OVSNet : Towards One-Pass Real-Time Video Object Segmentation
Video object segmentation aims at accurately segmenting the target object
regions across consecutive frames. It is technically challenging for coping
with complicated factors (e.g., shape deformations, occlusion and out of the
lens). Recent approaches have largely solved them by using backforth
re-identification and bi-directional mask propagation. However, their methods
are extremely slow and only support offline inference, which in principle
cannot be applied in real time. Motivated by this observation, we propose a
efficient detection-based paradigm for video object segmentation. We propose an
unified One-Pass Video Segmentation framework (OVS-Net) for modeling
spatial-temporal representation in a unified pipeline, which seamlessly
integrates object detection, object segmentation, and object re-identification.
The proposed framework lends itself to one-pass inference that effectively and
efficiently performs video object segmentation. Moreover, we propose a
maskguided attention module for modeling the multi-scale object boundary and
multi-level feature fusion. Experiments on the challenging DAVIS 2017
demonstrate the effectiveness of the proposed framework with comparable
performance to the state-of-the-art, and the great efficiency about 11.5 FPS
towards pioneering real-time work to our knowledge, more than 5 times faster
than other state-of-the-art methods.Comment: 10 pages, 6 figure
Instance-Level Salient Object Segmentation
Image saliency detection has recently witnessed rapid progress due to deep
convolutional neural networks. However, none of the existing methods is able to
identify object instances in the detected salient regions. In this paper, we
present a salient instance segmentation method that produces a saliency mask
with distinct object instance labels for an input image. Our method consists of
three steps, estimating saliency map, detecting salient object contours and
identifying salient object instances. For the first two steps, we propose a
multiscale saliency refinement network, which generates high-quality salient
region masks and salient object contours. Once integrated with multiscale
combinatorial grouping and a MAP-based subset optimization framework, our
method can generate very promising salient object instance segmentation
results. To promote further research and evaluation of salient instance
segmentation, we also construct a new database of 1000 images and their
pixelwise salient instance annotations. Experimental results demonstrate that
our proposed method is capable of achieving state-of-the-art performance on all
public benchmarks for salient region detection as well as on our new dataset
for salient instance segmentation.Comment: To appear in CVPR201
Neural Nets and Star/Galaxy Separation in Wide Field Astronomical Images
One of the most relevant problems in the extraction of scientifically useful
information from wide field astronomical images (both photographic plates and
CCD frames) is the recognition of the objects against a noisy background and
their classification in unresolved (star-like) and resolved (galaxies) sources.
In this paper we present a neural network based method capable to perform both
tasks and discuss in detail the performance of object detection in a
representative celestial field. The performance of our method is compared to
that of other methodologies often used within the astronomical community.Comment: 6 pages, to appear in the proceedings of IJCNN 99, IEEE Press, 199
Deep Memory Networks for Attitude Identification
We consider the task of identifying attitudes towards a given set of entities
from text. Conventionally, this task is decomposed into two separate subtasks:
target detection that identifies whether each entity is mentioned in the text,
either explicitly or implicitly, and polarity classification that classifies
the exact sentiment towards an identified entity (the target) into positive,
negative, or neutral.
Instead, we show that attitude identification can be solved with an
end-to-end machine learning architecture, in which the two subtasks are
interleaved by a deep memory network. In this way, signals produced in target
detection provide clues for polarity classification, and reversely, the
predicted polarity provides feedback to the identification of targets.
Moreover, the treatments for the set of targets also influence each other --
the learned representations may share the same semantics for some targets but
vary for others. The proposed deep memory network, the AttNet, outperforms
methods that do not consider the interactions between the subtasks or those
among the targets, including conventional machine learning methods and the
state-of-the-art deep learning models.Comment: Accepted to WSDM'1
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